§ 1.1 Field of the Invention
The present invention concerns advertising. In particular, the present invention concerns improving targeted advertising.
§ 1.2 Background
Interactive advertising provides opportunities for advertisers to target their ads to a receptive audience. That is, targeted ads are more likely to be useful to end users since the ads may be relevant to a need inferred from some user activity (e.g., relevant to a user's search query to a search engine, relevant to content in a document requested by the user, etc.). Query keyword relevant advertising has been used by search engines. The AdWords advertising system by Google of Mountain View, Calif. is one example of query keyword relevant advertising. Similarly, content-relevant advertising systems, such as the AdSense advertising system by Google for example, have been used. For example, U.S. patent application Ser. No. 10/314,427 (incorporated herein by reference and referred to as “the '427 application”) titled “METHODS AND APPARATUS FOR SERVING RELEVANT ADVERTISEMENTS”, filed on Dec. 6, 2002 and listing Jeffrey A. Dean, Georges R. Harik and Paul Bucheit as inventors, and Ser. No. 10/375,900 (incorporated by reference and referred to as “the '900 application”) titled “SERVING ADVERTISEMENTS BASED ON CONTENT,” filed on Feb. 26, 2003 and listing Darrell Anderson, Paul Bucheit, Alex Carobus, Claire Cui, Jeffrey A. Dean, Georges R. Harik, Deepak Jindal and Narayanan Shivakumar as inventors, describe methods and apparatus for serving ads relevant to the content of a document, such as a Web page for example.
When ads are to be served using some measure of their relevance to document, relevance information about the document is needed. Such relevance information may be determined from information intrinsic to the document, such as content extracted from the document. For example, concepts or topics may be determined using the content of the document. The document may also be assigned to one or more clusters. (See, e.g., U.S. Provisional Application Ser. No. 60/416,144 (incorporated herein by reference), titled “METHODS AND APPARATUS FOR PROBALISTIC HIERARCHICAL INFERENTIAL LEARNER,” filed on Oct. 3, 2003 In another example, feature vectors may be used to represent the occurrence of words and/or phrases in the document. Although such techniques for determining relevance information for documents have worked well, it is desirable to be able to provide additional relevance information, and/or to refine the relevance information to make it more useful.
Further if ads are to be associated with categories (e.g., for targeting to document categories, for association with categorical listings, etc.) it would be useful to develop and/or test such associations. Similarly, if query terms are to be associated with categories (e.g., for generating a categorized result page in response to a search query), it would be useful to develop and/or test such associations.
In view of the foregoing, it would be useful to expand and/or refine document and/or category relevance information. More generally, it would be useful to associate features with entities, such as documents, categories, etc. It would also be useful to score (e.g., weight) such associations.